Study on the prediction method of grasshopper occurrence risk in Inner Mongolia based on the maximum entropy model during the growing period

Fu Wen; Ronghao Liu; Axel Garcia Y Garcia; Huichun Ye; Longhui Lu; Eerdeng Qimuge; Zhongxiang Sun; Chaojia Nie; Xuemei Han; Yue Zhang

doi:10.1093/jee/toae036

Study on the prediction method of grasshopper occurrence risk in Inner Mongolia based on the maximum entropy model during the growing period

J Econ Entomol. 2024 Mar 16:toae036. doi: 10.1093/jee/toae036. Online ahead of print.

Authors

Fu Wen^{1

2}, Ronghao Liu², Axel Garcia Y Garcia^{3

4}, Huichun Ye^{1

5

6}, Longhui Lu^{1

5}, Eerdeng Qimuge⁷, Zhongxiang Sun⁸, Chaojia Nie^{1

5}, Xuemei Han^{1

9}, Yue Zhang^{1

2}

Affiliations

¹ International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China.
² College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
³ Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA.
⁴ Southwest Research and Outreach Center, University of Minnesota, Lamberton, MN 56152, USA.
⁵ Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
⁶ Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China.
⁷ Grassland Workstation of Xilingol League, Xilinhot 026000, China.
⁸ China Agricultural Museum, Beijing 100125, China.
⁹ School of Geology and Mining Engineering, Xinjiang University, Urumqi 830046, China.

PMID: 38493360
DOI: 10.1093/jee/toae036

Abstract

Grasshoppers represent a significant biological challenge in Inner Mongolia's grasslands, severely affecting the region's animal husbandry. Thus, dynamic monitoring of grasshopper infestation risk is crucial for sustainable livestock farming. This study employed the Maxent model, along with remote sensing data, to forecast Oedaleus decorus asiaticus occurrence during the growing season, using grasshopper suitability habitats as a base. The Maxent model's predictive accuracy was high, with an AUC of 0.966. The most influential environmental variables for grasshopper distribution were suitable habitat data (34.27%), the temperature-vegetation dryness index during the spawning period (18.81%), and various other meteorological and vegetation factors. The risk index model was applied to calculate the grasshopper distribution across different risk levels for the years 2019-2022. The data indicated that the level 1 risk area primarily spans central, eastern, and southwestern Inner Mongolia. By examining the variable weights, the primary drivers of risk level fluctuation from 2019 to 2022 were identified as accumulated precipitation and land surface temperature anomalies during the overwintering period. This study offers valuable insights for future O. decorus asiaticus monitoring in Inner Mongolia.

Keywords: Maxent; influencing factor; insect dynamics; predictive modeling; remote sensing.

Abstract

Grants and funding